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6d70a1d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | """
Data preprocessing and feature engineering utilities for job failure prediction.
"""
import pandas as pd
import numpy as np
from typing import Dict, List, Optional
import json
def parse_duration(x) -> float:
"""Parse duration string (HH:MM:SS) or numeric to seconds."""
if pd.isna(x):
return 0.0
try:
if isinstance(x, str):
parts = x.split(':')
if len(parts) == 3:
h, m, s = map(int, parts)
return h * 3600 + m * 60 + s
return float(x)
return float(x)
except (ValueError, AttributeError):
return 0.0
def engineer_features(df: pd.DataFrame) -> pd.DataFrame:
"""
Engineer features from raw job data.
Args:
df: DataFrame with columns: zone, job_nm, tasksgroup_nm, round_time,
job_start_time, job_end_time, duration, status, err_msg, zeppelin,
ictrl_dt, start_ictrl_dt, end_ictrl_dt
Returns:
DataFrame with engineered features
"""
df = df.copy()
# Parse timestamps
df['job_start_time'] = pd.to_datetime(df['job_start_time'], errors='coerce')
df['job_end_time'] = pd.to_datetime(df['job_end_time'], errors='coerce')
# Parse duration to seconds
if 'duration' in df.columns:
df['duration_sec'] = df['duration'].apply(parse_duration)
else:
df['duration_sec'] = 0.0
df['duration_sec'] = df['duration_sec'].fillna(0.0)
# Ground truth label
# Handle various success statuses: 'SUCCESS', 'SUCCEED', 'SUCCEEDED'
# Everything else (FAILED, ABORT-AUTO, RUNNING, etc.) is considered a failure
status_upper = df['status'].fillna('').str.upper()
success_statuses = ['SUCCESS', 'SUCCEED', 'SUCCEEDED']
df['is_failed'] = (~status_upper.isin(success_statuses)).astype(int)
# Time features
df['run_hour'] = df['job_start_time'].dt.hour.fillna(0).astype(int)
df['run_dow'] = df['job_start_time'].dt.dayofweek.fillna(0).astype(int) # 0=Mon, 6=Sun
df['is_weekend'] = (df['run_dow'] >= 5).astype(int)
# Cyclical encoding for hour
df['hour_sin'] = np.sin(2 * np.pi * df['run_hour'] / 24)
df['hour_cos'] = np.cos(2 * np.pi * df['run_hour'] / 24)
# Error message features
df['err_msg_len'] = df['err_msg'].fillna('').str.len()
df['has_err_msg'] = (df['err_msg_len'] > 0).astype(int)
# Zeppelin flag
df['is_zeppelin'] = df['zeppelin'].notna().astype(int)
# Job-level rolling statistics (per job_nm)
df = df.sort_values(['job_nm', 'job_start_time']).reset_index(drop=True)
df['failure_rate_7'] = df.groupby('job_nm')['is_failed'].transform(
lambda s: s.rolling(7, min_periods=1).mean()
)
df['avg_duration_7'] = df.groupby('job_nm')['duration_sec'].transform(
lambda s: s.rolling(7, min_periods=1).mean()
)
# Duration z-score (relative to rolling average)
df['duration_zscore'] = (
(df['duration_sec'] - df['avg_duration_7']) /
df['avg_duration_7'].replace(0, 1)
)
df['duration_zscore'] = df['duration_zscore'].fillna(0.0)
return df
def get_feature_columns() -> Dict[str, List[str]]:
"""Return feature column definitions."""
return {
'numeric': [
'duration_sec',
'duration_zscore',
'avg_duration_7',
'failure_rate_7',
'err_msg_len',
'hour_sin',
'hour_cos'
],
'categorical': [
'job_nm',
'tasksgroup_nm',
'zone',
'is_zeppelin',
'is_weekend'
],
'anomaly_numeric': [
'duration_sec',
'duration_zscore',
'avg_duration_7',
'failure_rate_7',
'err_msg_len',
'hour_sin',
'hour_cos'
]
}
def save_feature_schema(output_path: str = 'models/feature_schema.json'):
"""Save feature schema to JSON file."""
import os
os.makedirs(os.path.dirname(output_path), exist_ok=True)
schema = {
'feature_columns': get_feature_columns(),
'required_fields': [
'zone', 'job_nm', 'tasksgroup_nm', 'job_start_time',
'duration', 'status', 'err_msg', 'zeppelin'
]
}
with open(output_path, 'w') as f:
json.dump(schema, f, indent=2)
return schema
def align_schema_df(df: pd.DataFrame, schema_path: str = 'models/feature_schema.json') -> pd.DataFrame:
"""
Align DataFrame to feature schema, filling missing columns with defaults.
Args:
df: Input DataFrame
schema_path: Path to feature schema JSON
Returns:
Aligned DataFrame
"""
try:
with open(schema_path, 'r') as f:
schema = json.load(f)
except FileNotFoundError:
# If schema doesn't exist, engineer features and create it
df = engineer_features(df)
schema = save_feature_schema(schema_path)
# Ensure all required numeric and categorical columns exist
all_features = schema['feature_columns']['numeric'] + schema['feature_columns']['categorical']
for col in all_features:
if col not in df.columns:
if col in schema['feature_columns']['numeric']:
df[col] = 0.0
else:
df[col] = ''
return df
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